1. Field of the Invention
This invention relates to a method and apparatus for determining the relative time delay between two wideband signals, and is particularly but not exclusively applicable to estimating the line-of-bearing of a non-cooperative source of acoustic energy by determining the delay between two replicas of a wideband signal generated by the source and captured by a pair of collocated sensors.
2. Description of the Prior Art
There are many circumstances in which there is a need to detect and localize a noncooperative object of interest in some specified surveillance area. Such tasks can be performed by suitable active or passive sensors which can extract useful information by collaborative processing of signals reflected or emitted by that object.
In contrast to applications employing active sensors, such as radar or active sonar, in which the surveillance region of interest is illuminated by an interrogating energy waveform to obtain object-backscattered returns, passive sensors capture only object-generated signals (or object-influenced signals from separate sources). For example, the movement of people, vehicles, speedboats or vibrating machinery can all generate wideband acoustic signals, which can be exploited for object detection and localization.
As will be described in more detail below, an example in which object detection and localization is useful is that of security surveillance with a network of distributed acoustic sensors. When an object of interest, such as a vehicle, has been detected and localized, the estimated object position can be utilized by security cameras for aiming and zooming, in order to enhance the quality of recorded images. Such systems may be installed for monitoring purposes in industrial environments, e.g. to offer improved continuous surveillance of critical infrastructure, including power grids, power plants, gas and oil pipelines and water supply systems.
Another application is that of coastguard or littoral surveillance in which speedboats and other surface vessels of interest can be detected and localized by a network of floating buoys employing acoustic sensors.
In addition to the above surveillance and reconnaissance applications, in multimedia applications distributed microphone networks are capable of enhancing audio signals for improved intelligibility, and cuing for camera aiming.
Object-generated acoustic signals are classified as wideband signals since the ratio of their highest frequency component to lowest frequency component is relatively large. For example, for the audio range, 30 Hz to 15 kHz, the ratio is 500. In a case of motorized vehicles, dominant frequency components may range from 20 Hz to 2 kHz, resulting in a ratio of 100.
When the distance between an acoustic source and the sensors is large, the direction of wave propagation is approximately equal at each sensor (the far-field condition), and the propagating field consists of planar waves. Thus, for a far-field source, the angle of arrival (AOA) or direction-of-arrival (DOA) in the coordinate system of the sensors can be estimated directly from the relative delays of signals captured by sensors at different locations. Such relative delay is commonly referred to as the time-difference of arrival, or simply TDOA.
The angle of arrival (AOA) measurement restricts the location of the source along a line at the estimated angle of arrival (AOA). When multiple angle of arrival (AOA) measurements are made simultaneously by multiple spatially-separated sensors, a triangulation method may be used to determine the location of the source at the intersection of these lines-of-bearing.
FIG. 1 illustrates schematically a method of determining the angle of arrival θ from the time-difference of arrival (TDOA). As shown, a planar wave generated by a remote acoustic source reaches sensor SY first, and then sensor SX. From geometrical considerations, it follows that the angle of arrival (AOA) θ can be calculated from
  θ  =            arcsin      ⁡              (                  H          L                )              =          arcsin      ⁡              (                                            τ              0                        ⁢                          v              s                                L                )            where L is the distance between sensors, H represents the additional path length to sensor SX as referenced to sensor SY, τ0 is the time-difference of arrival (TDOA), and vs is the speed of sound.
For the purpose of angle of arrival (AOA) determination, a required time-difference of arrival (TDOA) estimate needs to be obtained from two signals, x(t) and y(t), captured by sensors SX and SY, respectively, wherex(t)=s(t)+nx(t) y(t)=s(t−τ0)+ny(t)where s(t) is an object-generated signal, τ0 denotes the time-difference of arrival (TDOA), and waveforms nx(t) and ny(t) represent background noise and other interference.
In the example shown in FIG. 1, the value τ0 is provided by a time-difference of arrival processor (TDOAP) crosscorrelating the wideband signals x(t) and y(t) captured by the sensors, i.e. by performing the operation
            R      xy        ⁡          (      τ      )        =            1      T        ⁢                  ∫        0        T            ⁢                        x          ⁡                      (                          t              -              τ                        )                          ⁢                  y          ⁡                      (            t            )                          ⁢                  ⅆ          t                    where the integral is evaluated over the observation interval of duration T and for a range, −|τmax|<τ<|τmax|, of time-difference of arrival (TDOA) values of interest. The value of argument τ that maximizes the crosscorrelation function Rxy(τ) provides an estimate of an unknown time-difference of arrival (TDOA).
The value of the time-difference of arrival (TDOA) supplied by the time-difference of arrival processor (TDOAP) is converted into a corresponding angle of arrival (AOA) θ by an angle calculator (ACR), which may, for example, be implemented in the form of a suitable look-up table stored in a read-only memory.
Acoustic signals emitted by objects of interest, in addition to occupying a wide frequency range, also manifest a nonstationary and chaotic nature with identifiable intermittent transients. As a result, many conventional crosscorrelation techniques based, explicitly or implicitly, on the assumptions of signal stationarity and noise Gaussianity are only of limited practical use. Furthermore, most practical implementations have to deal with discrete-time samples, so that the optimization procedures and performance analyses carried out in the continuous-time framework cannot be fully applicable.
U.S. Pat. No. 6,539,320 discloses a robust method for determining the delay between a primary reference signal and its time-delayed replica. In the following, the disclosed method will be referred to as “crosslation”, and a system implementing the method will be referred to as a “crosslator”. The contents of U.S. Pat. No. 6,539,320 are incorporated herein by reference. A crosslation technique involves using events (such as zero crossings) from one signal to sample the other signal. The events occur at irregular intervals, and are preferably at least substantially aperiodic. The samples are combined to derive a value which represents the extent to which the sampling coincides with features of the second signal corresponding to the events. By repeating this process for different delays between the first and second signals, it is possible to find the delay which gives rise to the value representing the greatest coincidence of events, i.e. the delay between the two signals.
In the example described in the above disclosure, a nondeterministic signal x(t) is subjected to an unknown delay to produce a signal y(t), and a reference version of the signal x(t) is examined to determine the time instants at which its level crosses zero, either with a positive slope (an upcrossing) or with a negative slope (a downcrossing). The time instants of these crossing events are used to obtain respective segments of the signal y(t), the segments having a predetermined duration. The segments corresponding to zero upcrossings are all summed, and the segments corresponding to zero downcrossings are all subtracted from the resulting sum. A representation of such segment combination is then examined to locate a feature in the form of an S-shaped odd function. In the following, the S-shaped odd function will be referred to as the crosslation function.
The position within the representation of a zero-crossing in the centre of the crosslation function represents the amount of the mutual delay between the two signals being processed. FIG. 3 shows an example of an S-shaped crosslation function obtained experimentally by processing a random binary waveform and its time-delayed replica.
FIG. 2 shows one possible example of exploiting the concept of crosslation to construct a system capable of determining the delay between a nondeterministic signal x(t) and its time-delayed replica y(t). The signal y(t) is the sum of noise ny(t) and the signal x(t) attenuated by the factor of α and delayed by τ0, hencey(t)=αx(t−τ0)+ny(t)
As shown in FIG. 2, the signal y(t) is converted by a hard limiter HY into a corresponding binary bipolar waveform which is applied to the input of a tapped delay line TDY. The delay line TDY comprises a cascade of M identical unit-delay cells D1, D2, . . . , DJ, . . . , DM. Each cell provides a suitably delayed output signal and also its polarity-reversed replica supplied by inverter IR.
The parallel outputs of the tapped delay line TDY are connected through a bank of switches BS to M averaging or integrating units AVG that accumulate data supplied by the tapped delay line TDY. The switches, normally open, are closed when a suitable signal is applied to their common control input. The time interval during which the switches are closed should be sufficiently long so that each new incremental signal sample can be acquired with minimal loss.
The time instants, at which the switches are closed and new data supplied to the averaging units, are determined by a zero-crossing detector ZCD that detects the crossings of zero level of a binary waveform obtained from the reference signal x(t) processed by a hard limiter HX; the resulting binary waveform is then delayed by a constant-delay line CDX. The value of the constant delay is equal to or greater than the expected maximum value of time delay to be determined. It should be pointed out that the averaging units receive the incremental input values from the tapped delay line TDY in a non-uniform manner, at the time instants coinciding with zero crossings of the delayed reference signal x(t).
Each time a zero upcrossing occurs, there appears transiently at the inputs of the averaging units a replica of a respective segment of the binary waveform obtained from the signal y(t). Similarly, each time a zero downcrossing occurs, there appears transiently at the inputs of the averaging units a reversed-polarity replica of a respective segment of the binary waveform obtained from the signal y(t). The averaging units thus combine the two groups of these segments to produce a representation of a combined waveform, like that of FIG. 3.
The signals obtained at the outputs of the averaging units AVG are used by the data processor. The operations performed by the data processor are so defined and structured as to determine the location of the zero crossing situated between the two opposite-polarity main peaks exhibited by the resulting S-shaped crosslation function. The location of this zero crossing corresponds to the time delay between the signals x(t) and y(t). A set of suitable operations and their sequence can be constructed by anyone skilled in the art.
In order to simplify the structure of a crosslator system, instead of using both upcrossings and downcrossings, the reference version of a wideband non-deterministic signal x(t) can be examined to determine the time instants of zero upcrossings (or downcrossings) only. However, irrespective of the particular arrangement used, a crosslation-based technique always includes a step of determining the time instants at which a reference signal crosses a predetermined threshold. Those specific time instants are also referred to as significant events. In a hardware implementation of crosslation significant events define the time instants at which suitable trigger pulses are generated.
The crosslation techniques of U.S. Pat. No. 6,539,320 for time-delay determination are robust and particularly useful for processing non-Gaussian signals. However, crosslation in the disclosed form is not well suited to time-difference of arrival (TDOA) determination.
It would therefore be desirable to provide a method and an apparatus for determining time-difference of arrival (TDOA) in a more efficient way than that provided by the prior art techniques.
It would also be desirable to provide a time delay measurement technique which is less subject to noise, and more suited to detecting angle of arrival than prior art arrangements.